{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Sequential Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A matplotlib utility function for visualizing results from [`feature_selection.SequentialFeatureSelector`](`../feature_selection/SequentialFeatureSelector.md`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.plotting import plot_sequential_feature_selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for more information on sequential feature selection, please see [`feature_selection.SequentialFeatureSelector`](`../feature_selection/SequentialFeatureSelector.md`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Plotting the results from SequentialFeatureSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Features: 4/4"
     ]
    },
    {
     "data": {
      "image/png": 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L+hs3d/PwU+Fsk2r1pDUfjqbbCtXunXCEnfbxA8uWLUs6hBGrNxfHlu5i35Tn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\nIkNKvCIC4O4/jOYM+TSh++Rm4CB3fyxaZS6wR2z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      "text/plain": [
       "<matplotlib.figure.Figure at 0x105fea3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "knn = KNeighborsClassifier(n_neighbors=4)\n",
    "\n",
    "sfs = SFS(knn, \n",
    "          k_features=4, \n",
    "          forward=True, \n",
    "          floating=False, \n",
    "          scoring='accuracy',\n",
    "          cv=5)\n",
    "\n",
    "sfs = sfs.fit(X, y)\n",
    "\n",
    "fig1 = plot_sfs(sfs.get_metric_dict(), kind='std_dev')\n",
    "\n",
    "plt.ylim([0.8, 1])\n",
    "plt.title('Sequential Forward Selection (w. StdDev)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_sequential_feature_selection\n",
      "\n",
      "*plot_sequential_feature_selection(metric_dict, kind='std_dev', color='blue', bcolor='steelblue', marker='o', alpha=0.2, ylabel='Performance', confidence_interval=0.95)*\n",
      "\n",
      "Plot feature selection results.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `metric_dict` : mlxtend.SequentialFeatureSelector.get_metric_dict() object\n",
      "\n",
      "\n",
      "- `kind` : str (default: \"std_dev\")\n",
      "\n",
      "    The kind of error bar or confidence interval in\n",
      "    {'std_dev', 'std_err', 'ci', None}.\n",
      "\n",
      "- `color` : str (default: \"blue\")\n",
      "\n",
      "    Color of the lineplot (accepts any matplotlib color name)\n",
      "\n",
      "- `bcolor` : str (default: \"steelblue\").\n",
      "\n",
      "    Color of the error bars / confidence intervals\n",
      "    (accepts any matplotlib color name).\n",
      "\n",
      "- `marker` : str (default: \"o\")\n",
      "\n",
      "    Marker of the line plot\n",
      "    (accepts any matplotlib marker name).\n",
      "\n",
      "- `alpha` : float in [0, 1] (default: 0.2)\n",
      "\n",
      "    Transparency of the error bars / confidence intervals.\n",
      "\n",
      "- `ylabel` : str (default: \"Performance\")\n",
      "\n",
      "    Y-axis label.\n",
      "\n",
      "- `confidence_interval` : float (default: 0.95)\n",
      "\n",
      "    Confidence level if `kind='ci'`.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `fig` : matplotlib.pyplot.figure() object\n",
      "\n",
      "\n",
      "**Examples**\n",
      "\n",
      "For usage examples, please see\n",
      "    [http://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_sequential_feature_selection/)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_sequential_feature_selection.md', 'r') as f:\n",
    "    s = f.read()\n",
    "print(s)"
   ]
  }
 ],
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   "language": "python",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "version": "3.6.4"
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  "toc": {
   "nav_menu": {},
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